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International Symposium on String Processing and Information Retrieval

SPIRE 2017: String Processing and Information Retrieval pp 241–253Cite as

Optimal Skeleton Huffman Trees

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10508))

Abstract

A skeleton Huffman tree is a Huffman tree from which all complete subtrees of depth \(h \ge 1\) have been pruned. Skeleton Huffman trees are used to save storage and enhance processing time in several applications such as decoding, compressed pattern matching and Wavelet trees for random access. However, the straightforward way of basing the construction of a skeleton tree on a canonical Huffman tree does not necessarily yield the least number of nodes. The notion of optimal skeleton trees is introduced, and an algorithm for achieving such trees is investigated. The resulting more compact trees can be used to further enhance the time and space complexities of the corresponding algorithms.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Bar Ilan University, 52900, Ramat Gan, Israel

    Shmuel T. Klein

  2. Department of Computer Science, Ariel University, 40700, Ariel, Israel

    Tamar C. Serebro & Dana Shapira

Authors
  1. Shmuel T. Klein
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  2. Tamar C. Serebro
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  3. Dana Shapira
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Corresponding author

Correspondence to Shmuel T. Klein .

Editor information

Editors and Affiliations

  1. Università di Palermo, Palermo, Italy

    Gabriele Fici

  2. Università di Palermo, Palermo, Italy

    Marinella Sciortino

  3. Università di Pisa, Pisa, Italy

    Rossano Venturini

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© 2017 Springer International Publishing AG

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Klein, S.T., Serebro, T.C., Shapira, D. (2017). Optimal Skeleton Huffman Trees. In: Fici, G., Sciortino, M., Venturini, R. (eds) String Processing and Information Retrieval. SPIRE 2017. Lecture Notes in Computer Science(), vol 10508. Springer, Cham. https://doi.org/10.1007/978-3-319-67428-5_21

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  • DOI: https://doi.org/10.1007/978-3-319-67428-5_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67427-8

  • Online ISBN: 978-3-319-67428-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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